Vehicle Re-Identification
53 papers with code • 12 benchmarks • 9 datasets
Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.
( Image credit: A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras )
Benchmarks
These leaderboards are used to track progress in Vehicle Re-Identification
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Libraries
Use these libraries to find Vehicle Re-Identification models and implementationsDatasets
VehicleID
VeRi-776
CityFlow
VeRi-Wild
VehicleX
VRAI
Vehicle-Rear
Vehicle-1M
Car datasets in multiple scenes
Most implemented papers
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.
VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification
layumi/Person_reID_baseline_pytorch
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This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
TransReID: Transformer-based Object Re-Identification
heshuting555/TransReID
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ICCV 2021
Extracting robust feature representation is one of the key challenges in object re-identification (ReID).
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
layumi/Person_reID_baseline_pytorch
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This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
FastReID: A Pytorch Toolbox for General Instance Re-identification
General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research.
Cluster Contrast for Unsupervised Person Re-Identification
Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.
Simulating Content Consistent Vehicle Datasets with Attribute Descent
yorkeyao/VehicleX
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ECCV 2020
Between synthetic and real data, there is a two-level domain gap, i. e., content level and appearance level.
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
heshuting555/AICITY2020_DMT_VehicleReID
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Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
Andrew-Brown1/Smooth_AP
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ECCV 2020
Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.
